HYBRID DIMENSIONALITY REDUCTION METHOD USING KAISER COMPONENT ANALYSIS AND INDEPENDENT COMPONENT ANALYSIS
Journal Title: GRD Journal for Engineering - Year 2016, Vol 1, Issue 0
Abstract
Dimensionality Reduction is the process of extracting the more relevant information. Conventional dimensionality reduction is categorized into two methods like Stand alone and Hybrid method. Standalone dimensionality reduction reduces the dimensions based on a single criterion whereas Hybrid method combines two or more criterion. In this paper, we proposed new hybrid method for dimensionality reduction Using Kaiser Component Analysis (KCA) and Independent Component Analysis (ICA). Kaiser component Analysis extracts the uncorrelated information and Independent Component Analysis maximizing the Independency among the data. The hybrid method using these Kaiser Component Analysis and Independent Component Analysis achieves both correlations and Independency among the Information and it is applied on SVM classification. The result improves the accuracy of the classification.
Authors and Affiliations
K. Arunasakthi, J. Sulthan Alikhan
Keywords
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